Point cloud completion is a generation and estimation issue derived from the partial point
clouds, which plays a vital role in the applications of 3D computer vision. The progress of …

Powerful priors allow us to perform inference with insufficient information. In this paper, we
propose an autoregressive prior for 3D shapes to solve multimodal 3D tasks such as shape …

Point cloud completion aims to predict a complete shape in high accuracy from its partial
observation. However, previous methods usually suffered from discrete nature of point cloud …

In autonomous driving, a LiDAR-based object detector should perform reliably at different
geographic locations and under various weather conditions. While recent 3D detection …

Chamfer Distance (CD) and Earth Mover's Distance (EMD) are two broadly adopted metrics
for measuring the similarity between two point sets. However, CD is usually insensitive to …

Point clouds produced by 3D scanning are often sparse, non-uniform, and noisy. Recent
upsampling approaches aim to generate a dense point set, while achieving both distribution …

Most 3D shape completion approaches rely heavily on partial-complete shape pairs and
learn in a fully supervised manner. Despite their impressive performances on in-domain …

Self-supervised learning (SSL) is a technique for learning useful representations from
unlabeled data. It has been applied effectively to domain adaptation (DA) on images and …

There is a growing number of tasks that work directly on point clouds. As the size of the point
cloud grows, so do the computational demands of these tasks. A possible solution is to …

In this paper, we present a novel unpaired point cloud completion network, named
Cycle4Completion, to infer the complete geometries from a partial 3D object. Previous …